Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2102.04279	Constrained Ensemble Langevin Monte Carlo	The classical Langevin Monte Carlo method looks for samples from a target distribution by descending the samples along the gradient of the target distribution. The method enjoys a fast convergence rate. However, the numerical cost is sometimes high because each iteration requires the computation of a gradient. One appr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	219,057
2106.05345	Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud	With a growing demand for adopting ML models for a varietyof application services, it is vital that the frameworks servingthese models are capable of delivering highly accurate predic-tions with minimal latency along with reduced deploymentcosts in a public cloud environment. Despite high latency,prior works in this do...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	240,056
2403.06940	Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction	Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to its progression. Accurately forecasting CTh trajectories can significantly enhance early diagnosis and intervention strategies, providing tim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	436,670
2501.16966	Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning	Federated Learning (FL) empowers multiple clients to collaboratively train machine learning models without sharing local data, making it highly applicable in heterogeneous Internet of Things (IoT) environments. However, intrinsic heterogeneity in clients' model architectures and computing capabilities often results in ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,168
2108.03941	Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO	In a time-varying massive multiple-input multipleoutput (MIMO) system, the acquisition of the downlink channel state information at the base station (BS) is a very challenging task due to the prohibitively high overheads associated with downlink training and uplink feedback. In this paper, we consider the hybrid precod...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	249,836
2108.06709	SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation	In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection research focuses on improving performance within a single domain, our study reveals that the performance of modern detectors can drop drastically...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,694
2012.09938	Can Transformers Reason About Effects of Actions?	A recent work has shown that transformers are able to "reason" with facts and rules in a limited setting where the rules are natural language expressions of conjunctions of conditions implying a conclusion. Since this suggests that transformers may be used for reasoning with knowledge given in natural language, we do a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	212,204
2011.00756	Observation Space Matters: Benchmark and Optimization Algorithm	Recent advances in deep reinforcement learning (deep RL) enable researchers to solve challenging control problems, from simulated environments to real-world robotic tasks. However, deep RL algorithms are known to be sensitive to the problem formulation, including observation spaces, action spaces, and reward functions....	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	204,354
2404.11771	IoT-Driven Cloud-based Energy and Environment Monitoring System for Manufacturing Industry	This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, hu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	447,603
1308.3831	Strict majority bootstrap percolation in the r-wheel	In this paper we study the strict majority bootstrap percolation process on graphs. Vertices may be active or passive. Initially, active vertices are chosen independently with probability p. Each passive vertex becomes active if at least half of its neighbors are active (and thereafter never changes its state). If at t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,505
2305.04047	Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising	Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,612
2305.05902	Multi-stage Progressive Reasoning for Dunhuang Murals Inpainting	Dunhuang murals suffer from fading, breakage, surface brittleness and extensive peeling affected by prolonged environmental erosion. Image inpainting techniques are widely used in the field of digital mural inpainting. Generally speaking, for mural inpainting tasks with large area damage, it is challenging for any imag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,334
2011.07952	Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations	Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algori...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,717
1912.01752	Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability	Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	156,164
2205.14234	Two-Leg Deep Space Relay Architectures: Performance, Challenges, and Perspectives	In this paper, architectures for interplanetary communications that feature the use of a data relay are investigated. In the considered "two-leg" architecture, a spacecraft orbiting the Earth, or in orbit at a Lagrange point, receives data from a deep space probe (leg-1) and relays them towards ground (leg-2). Differen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	299,266
2502.01639	SliderSpace: Decomposing the Visual Capabilities of Diffusion Models	We present SliderSpace, a framework for automatically decomposing the visual capabilities of diffusion models into controllable and human-understandable directions. Unlike existing control methods that require a user to specify attributes for each edit direction individually, SliderSpace discovers multiple interpretabl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	529,954
1405.2029	Mutual Information as a Figure of Merit for Optical Fiber Systems	Advanced channel decoders rely on soft-decision decoder inputs for which mutual information (MI) is the natural figure of merit. In this paper, we analyze an optical fiber system by evaluating MI as the maximum achievable rate of transmission of such a system. MI is estimated by means of histograms for which the correc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,940
2108.03594	MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting	Traffic forecasting is a core element of intelligent traffic monitoring system. Approaches based on graph neural networks have been widely used in this task to effectively capture spatial and temporal dependencies of road networks. However, these approaches can not effectively define the complicated network topology. B...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,717
2308.00683	CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code	Recent works have widely adopted large language model pretraining for source code, suggested source code-specific pretraining objectives and investigated the applicability of various Transformer-based language model architectures for source code. This work investigates another important aspect of such models, namely th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	383,015
2110.07905	Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector	Plasticity-stability dilemma is a main problem for incremental learning, where plasticity is referring to the ability to learn new knowledge, and stability retains the knowledge of previous tasks. Many methods tackle this problem by storing previous samples, while in some applications, training data from previous tasks...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	261,176
2408.09882	GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits	The restless multi-armed bandit (RMAB) framework is a popular model with applications across a wide variety of fields. However, its solution is hindered by the exponentially growing state space (with respect to the number of arms) and the combinatorial action space, making traditional reinforcement learning methods inf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,632
2109.06283	Graph Algorithms for Multiparallel Word Alignment	With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	255,095
2112.01705	Multilingual Text Classification for Dravidian Languages	As the fourth largest language family in the world, the Dravidian languages have become a research hotspot in natural language processing (NLP). Although the Dravidian languages contain a large number of languages, there are relatively few public available resources. Besides, text classification task, as a basic task o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	269,579
2112.09487	A controller for reaching and unveiling a partially occluded object of interest with an eye-in-hand robot	In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed.The control scheme is based only on the classified point cloud obtained by the in-hand camera attached to the robot's end effector. It is shown that the proposed controller reaches in the vicinity of the obj...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	272,168
2305.00918	CORSD: Class-Oriented Relational Self Distillation	Knowledge distillation conducts an effective model compression method while holding some limitations:(1) the feature based distillation methods only focus on distilling the feature map but are lack of transferring the relation of data examples; (2) the relational distillation methods are either limited to the handcraft...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	361,484
2201.00418	Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis	Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of Polycystic Ovary Syndrome also known as PCOS is an emerging problem in women aged fr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	273,958
2012.06181	Deep-Learning-Based Kinematic Reconstruction for DUNE	In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of $\theta_{23}$ remain unknown. The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment, which aims to address these questions by measuring th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,027
2210.11114	Pruning by Active Attention Manipulation	Filter pruning of a CNN is typically achieved by applying discrete masks on the CNN's filter weights or activation maps, post-training. Here, we present a new filter-importance-scoring concept named pruning by active attention manipulation (PAAM), that sparsifies the CNN's set of filters through a particular attention ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	325,188
1711.04248	Linking Sequences of Events with Sparse or No Common Occurrence across Data Sets	Data of practical interest - such as personal records, transaction logs, and medical histories - are sequential collections of events relevant to a particular source entity. Recent studies have attempted to link sequences that represent a common entity across data sets to allow more comprehensive statistical analyses a...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	84,363
2001.04218	Optimal Scheduling for Maximizing Information Freshness & System Performance in Industrial Cyber-Physical Systems	Age of Information is a newly introduced metric, getting vivid attention for measuring the freshness of information in real-time networks. This parameter has evolved to guarantee the reception of timely information from the latest status update, received by a user from any real-time application. In this paper, we study...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	160,185
2404.04905	Review for Handling Missing Data with special missing mechanism	Missing data poses a significant challenge in data science, affecting decision-making processes and outcomes. Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	444,854
2308.09102	Universal and Automatic Elbow Detection for Learning the Effective Number of Components in Model Selection Problems	We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED does not require the knowledge of a likelihood function and can be easi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	386,154
2310.00035	LoRA ensembles for large language model fine-tuning	Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution samples. One approach commonly used in vision for alleviating this issue is a deep ensemble, which constructs an ensemble by training the s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	395,797
1804.01292	Nonexistence of generalized bent functions and the quadratic norm form equations	We present a new result on the nonexistence of generalized bent functions (GBFs)from (Z/tZ)^n to Z/tZ (called type [n, t]) for a large class. Assume p is an odd prime number. By showing certain quadratic norm form equations having no integral points, we obtain a universalresult on the nonexistence of GBFs with type [n,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	94,202
1707.09410	A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously	Capabilities of detecting temporal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts, and these...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	77,994
1909.02373	LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport	Estimating mutual information is an important statistics and machine learning problem. To estimate the mutual information from data, a common practice is preparing a set of paired samples $\{(\mathbf{x}_i,\mathbf{y}_i)\}_{i=1}^n \stackrel{\mathrm{i.i.d.}}{\sim} p(\mathbf{x},\mathbf{y})$. However, in many situations, it...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,179
1503.03199	Persistence of activity on Twitter triggered by a natural disaster: A data analysis	In this note, we list the results of a simple analysis of a Twitter dataset: the complete dataset of Japanese tweets in the 1-week period after the Great East Japan earthquake, which occurred on March 11, 2011. Our data analysis shows how people reacted to the earthquake on Twitter and how some users went inactive in t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	41,025
1110.0560	Easily Computed Lower Bounds on the Information Rate of Intersymbol Interference Channels	Provable lower bounds are presented for the information rate I(X; X+S+N) where X is the symbol drawn independently and uniformly from a finite-size alphabet, S is a discrete-valued random variable (RV) and N is a Gaussian RV. It is well known that with S representing the precursor intersymbol interference (ISI) at the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,467
cs/0308019	Language Access: An Information Based Approach	The anusaaraka system (a kind of machine translation system) makes text in one Indian language accessible through another Indian language. The machine presents an image of the source text in a language close to the target language. In the image, some constructions of the source language (which do not have equivalents i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,959
1806.08238	Development of Robust Fractional-Order Reset Control	In this paper, a framework for the combination of robust fractional order CRONE control with non-linear reset is given for both first and second generation CRONE control. General design rules are derived and presented for these CRONE reset controllers. Within this framework, fractional order control allows for better t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	101,121
2409.17576	ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition	Synthetic face recognition (SFR) aims to generate synthetic face datasets that mimic the distribution of real face data, which allows for training face recognition models in a privacy-preserving manner. Despite the remarkable potential of diffusion models in image generation, current diffusion-based SFR models struggle...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,874
2308.10570	Self-Feedback DETR for Temporal Action Detection	Temporal Action Detection (TAD) is challenging but fundamental for real-world video applications. Recently, DETR-based models have been devised for TAD but have not performed well yet. In this paper, we point out the problem in the self-attention of DETR for TAD; the attention modules focus on a few key elements, calle...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,791
1201.1671	Error-Correcting Codes for Reliable Communications in Microgravity Platforms	The PAANDA experiment was conceived to characterize the acceleration ambient of a rocket launched microgravity platform, specially the microgravity phase. The recorded data was transmitted to ground stations, leading to loss of telemetry information sent during the reentry period. Traditionally, an error-correcting cod...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	13,731
2106.11250	VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning	Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence). To learn these interactions, we apply a mask-then-predict pre-training task on discretized video tokens generated via VQ-VAE. Unlike language, where the text...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	242,330
1802.07589	Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification	Deep convolutional neural networks provide a powerful feature learning capability for image classification. The deep image features can be utilized to deal with many image understanding tasks like image classification and object recognition. However, the robustness obtained in one dataset can be hardly reproduced in th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,925
2403.11529	Video Object Segmentation with Dynamic Query Modulation	Storing intermediate frame segmentations as memory for long-range context modeling, spatial-temporal memory-based methods have recently showcased impressive results in semi-supervised video object segmentation (SVOS). However, these methods face two key limitations: 1) relying on non-local pixel-level matching to read ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,744
2203.06973	Solving parametric partial differential equations with deep rectified quadratic unit neural networks	Implementing deep neural networks for learning the solution maps of parametric partial differential equations (PDEs) turns out to be more efficient than using many conventional numerical methods. However, limited theoretical analyses have been conducted on this approach. In this study, we investigate the expressive pow...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	285,291
1709.04514	Differentially Private Mixture of Generative Neural Networks	Generative models are used in a wide range of applications building on large amounts of contextually rich information. Due to possible privacy violations of the individuals whose data is used to train these models, however, publishing or sharing generative models is not always viable. In this paper, we present a novel ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	80,674
1503.05667	BitSim: An Algebraic Similarity Measure for Description Logics Concepts	In this paper, we propose an algebraic similarity measure {\sigma}BS (BS stands for BitSim) for assigning semantic similarity score to concept definitions in ALCH+ an expressive fragment of Description Logics (DL). We define an algebraic interpretation function, I_B, that maps a concept definition to a unique string ({...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	41,269
2104.06400	Mediators in Determining what Processing BERT Performs First	Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the pred...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,063
2110.01848	Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks	Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Determinis...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	258,916
2109.09551	Maximum Sum-Rank Distance Codes over Finite Chain Rings	In this work, maximum sum-rank distance (MSRD) codes and linearized Reed-Solomon codes are extended to finite chain rings. It is proven that linearized Reed-Solomon codes are MSRD over finite chain rings, extending the known result for finite fields. For the proof, several results on the roots of skew polynomials are e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	256,323
2307.12221	FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition	This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while e...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	381,186
2211.02625	MAEEG: Masked Auto-encoder for EEG Representation Learning	Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,634
1206.3278	Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression	Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distr...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	16,535
1703.00050	SceneSeer: 3D Scene Design with Natural Language	Designing 3D scenes is currently a creative task that requires significant expertise and effort in using complex 3D design interfaces. This effortful design process starts in stark contrast to the easiness with which people can use language to describe real and imaginary environments. We present SceneSeer: an interacti...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	69,093
2011.09982	On the Feasibility of Load-Changing Attacks in Power Systems during the COVID-19 Pandemic	The electric power grid is a complex cyberphysical energy system (CPES) in which information and communication technologies (ICT) are integrated into the operations and services of the power grid infrastructure. The growing number of Internet-of-things (IoT) high-wattage appliances, such as air conditioners and electri...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	207,383
2403.12029	Align and Distill: Unifying and Improving Domain Adaptive Object Detection	Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarking pitfalls that call past results into question and hamper further pr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	438,988
2204.04545	Self-Labeling Refinement for Robust Representation Learning with Bootstrap Your Own Latent	In this work, we have worked towards two major goals. Firstly, we have investigated the importance of Batch Normalisation (BN) layers in a non-contrastive representation learning framework called Bootstrap Your Own Latent (BYOL). We conducted several experiments to conclude that BN layers are not necessary for represen...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	290,696
1511.07896	Private Posterior distributions from Variational approximations	Privacy preserving mechanisms such as differential privacy inject additional randomness in the form of noise in the data, beyond the sampling mechanism. Ignoring this additional noise can lead to inaccurate and invalid inferences. In this paper, we incorporate the privacy mechanism explicitly into the likelihood functi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	49,476
2209.11436	Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation	The findings on open-set recognition (OSR) show that models trained on classification datasets are capable of detecting unknown classes not encountered during the training process. Specifically, after training, the learned representations of known classes dissociate from the representations of the unknown class, facili...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,186
1907.03240	Informative Visual Storytelling with Cross-modal Rules	Existing methods in the Visual Storytelling field often suffer from the problem of generating general descriptions, while the image contains a lot of meaningful contents remaining unnoticed. The failure of informative story generation can be concluded to the model's incompetence of capturing enough meaningful concepts....	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	137,815
1311.4276	Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population	Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can assist in identifying various patterns in human population. In this study, we present methods and algorithms which can assist in identifying variations in lifespan...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	28,481
2502.01276	HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance	Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance. However, the impact of individual hyperparameters on model generalization is highly context-dependent, prohibiting a one-size-fits-all solution and requiring opaque automated machine learning (AutoML) systems to find optimal...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	529,783
2212.09598	Query-as-context Pre-training for Dense Passage Retrieval	Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages from the same document to be relevant, without taking into account the possibility of weakly correlated pairs. Thus, this paper proposes query-...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	337,164
2411.02098	Low-Rank Tensors for Multi-Dimensional Markov Models	This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success of these matrix techniques, we present low-rank tensors for representing transiti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	505,350
2412.00441	Fine Grained Analysis and Optimization of Large Scale Automotive Radar Networks	Advanced driver assistance systems (ADAS) enabled by automotive radars have significantly enhanced vehicle safety and driver experience. However, the extensive use of radars in dense road conditions introduces mutual interference, which degrades detection accuracy and reliability. Traditional interference models are li...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	512,656
2411.14707	High-Bandwidth, Low-Computational Approach: Estimator-Based Control for Hybrid Flying Capacitor Multilevel Converters Using Multi-Cost Gradient Descent and State Feedforward	This paper presents an estimator-based control framework for hybrid flying capacitor multilevel (FCML) converters, achieving high-bandwidth control and reduced computational complexity. Utilizing a hybrid estimation method that combines closed-loop and open-loop dynamics, the proposed approach enables accurate and fast...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	510,283
2302.04060	A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios	Embedding-aware generative model (EAGM) addresses the data insufficiency problem for zero-shot learning (ZSL) by constructing a generator between semantic and visual feature spaces. Thanks to the predefined benchmark and protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue that it is time to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	344,572
2303.06171	DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference	Bayesian inference provides a principled framework for learning from complex data and reasoning under uncertainty. It has been widely applied in machine learning tasks such as medical diagnosis, drug design, and policymaking. In these common applications, data can be highly sensitive. Differential privacy (DP) offers d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,722
2108.10252	Federated Multi-Task Learning under a Mixture of Distributions	The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models. First efforts in FL focused on learning a single global model with good average performance across clients, but the glob...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,842
2401.12262	Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction	Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	423,327
2502.07181	Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations	This research addresses the challenge of limited data in tabular data classification, particularly prevalent in domains with constraints like healthcare. We propose Tab2Visual, a novel approach that transforms heterogeneous tabular data into visual representations, enabling the application of powerful deep learning mod...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	532,465
2112.02866	Nonstochastic Bandits with Composite Anonymous Feedback	We investigate a nonstochastic bandit setting in which the loss of an action is not immediately charged to the player, but rather spread over the subsequent rounds in an adversarial way. The instantaneous loss observed by the player at the end of each round is then a sum of many loss components of previously played act...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,001
2312.05283	Nuvo: Neural UV Mapping for Unruly 3D Representations	Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation techniques. As such, applying these methods to the volume densities recovered by neural radiance fields and related techniques (or meshes t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	414,021
2302.05779	How to prepare your task head for finetuning	In deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-tuning, as the pretrained and downstream tasks are usually different. Although there exist many different designs for finetuning, a full unde...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	345,165
2205.10014	A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection	Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure various deep graph learning algorithms behave in a socially responsible manner and meet regulatory compliance...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	297,515
1907.08549	Universality and individuality in neural dynamics across large populations of recurrent networks	Task-based modeling with recurrent neural networks (RNNs) has emerged as a popular way to infer the computational function of different brain regions. These models are quantitatively assessed by comparing the low-dimensional neural representations of the model with the brain, for example using canonical correlation ana...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	139,133
2301.05182	Thompson Sampling with Diffusion Generative Prior	In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	340,281
2409.17077	Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming	Dream11 is a fantasy sports platform that allows users to create their own virtual teams for real-life sports events. We host multiple sports and matches for our 200M+ user base. In this RMG (real money gaming) setting, users pay an entry amount to participate in various contest products that we provide to users. In ou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,638
1905.13402	Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks	Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging. We address these issues with a new model-based reinforcement learnin...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	133,114
2310.08312	GePSAn: Generative Procedure Step Anticipation in Cooking Videos	We study the problem of future step anticipation in procedural videos. Given a video of an ongoing procedural activity, we predict a plausible next procedure step described in rich natural language. While most previous work focus on the problem of data scarcity in procedural video datasets, another core challenge of fu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	399,346
1805.03511	Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems	Automated toll systems rely on proper classification of the passing vehicles. This is especially difficult when the images used for classification only cover parts of the vehicle. To obtain information about the whole vehicle. we reconstruct the vehicle as 3D object and exploit this additional information within a Conv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,064
2212.13205	Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters	When reading news articles on social networking services and news sites, readers can view comments marked by other people on these articles. By reading these comments, a reader can understand the public opinion about the news, and it is often helpful to grasp the overall picture of the news. However, these comments oft...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	338,246
1911.08024	A Bias Trick for Centered Robust Principal Component Analysis	Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a "bias trick" that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,062
2204.11923	Sparse-Dense Motion Modelling and Tracking for Manipulation without Prior Object Models	This work presents an approach for modelling and tracking previously unseen objects for robotic grasping tasks. Using the motion of objects in a scene, our approach segments rigid entities from the scene and continuously tracks them to create a dense and sparse model of the object and the environment. While the dense t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	293,313
2102.01116	Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence	Medical toxicology is the clinical specialty that treats the toxic effects of substances, be it an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	217,995
2411.19250	Parametric Lattices Are Better Quantizers in Dimensions 13 and 14	New lattice quantizers with lower normalized second moments than previously reported are constructed in 13 and 14 dimensions and conjectured to be optimal. Our construction combines an initial numerical optimization with a subsequent analytical optimization of families of lattices, whose Voronoi regions are constructed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	512,177
1909.08663	Do We Need Neural Models to Explain Human Judgments of Acceptability?	Native speakers can judge whether a sentence is an acceptable instance of their language. Acceptability provides a means of evaluating whether computational language models are processing language in a human-like manner. We test the ability of computational language models, simple language features, and word embeddings...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	146,025
2103.16120	Mixed-precision for Linear Solvers in Global Geophysical Flows	Semi-implicit time-stepping schemes for atmosphere and ocean models require elliptic solvers that work efficiently on modern supercomputers. This paper reports our study of the potential computational savings when using mixed precision arithmetic in the elliptic solvers. The essential components of a representative ell...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	227,467
2111.11292	Uncertainty Inequalities for 3D Octonionic-valued Signals Associated with Octonion Offset Linear Canonical Transform	he octonion offset linear canonical transform can be defined as a time shifted and frequency modulated version of the octonion linear canonical transform, a more general framework of most existing signal processing tools. In this paper, we first define the and provide its closed-form representation. Based on this fact,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	267,614
2103.16329	E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT	This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which c...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	227,544
2107.05677	Codified audio language modeling learns useful representations for music information retrieval	We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explore representations from Jukebox (Dhariwal et al. 2020): a music generation system containing a language model trained on codified audio from ...	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	245,847
2311.04918	Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?	Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, w...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	406,396
1912.13075	Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters	Federated learning is a distributed, privacy-aware learning scenario which trains a single model on data belonging to several clients. Each client trains a local model on its data and the local models are then aggregated by a central party. Current federated learning methods struggle in cases with heterogeneous client-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,998
1809.04864	The Covering Radius of the Reed--Muller Code $RM(2,7)$ is 40	It was proved by J. Schatz that the covering radius of the second order Reed--Muller code $RM(2, 6)$ is 18 (IEEE Trans Inf Theory 27: 529--530, 1985). However, the covering radius of $RM(2,7)$ has been an open problem for many years. In this paper, we prove that the covering radius of $RM(2,7)$ is 40, which is the same...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	107,669
2312.03002	The mechanistic basis of data dependence and abrupt learning in an in-context classification task	Transformer models exhibit in-context learning: the ability to accurately predict the response to a novel query based on illustrative examples in the input sequence. In-context learning contrasts with traditional in-weights learning of query-output relationships. What aspects of the training data distribution and archi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,083
2301.12166	Heterogeneous Datasets for Federated Survival Analysis Simulation	Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,424
2205.12230	Chunk-based Nearest Neighbor Machine Translation	Semi-parametric models, which augment generation with retrieval, have led to impressive results in language modeling and machine translation, due to their ability to retrieve fine-grained information from a datastore of examples. One of the most prominent approaches, $k$NN-MT, exhibits strong domain adaptation capabili...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	298,451
2010.12889	Force and state-feedback control for robots with non-collocated environmental and actuator forces	In this paper, we present an impedance control design for multi-variable linear and nonlinear robotic systems. The control design considers force and state feedback to improve the performance of the closed loop. Simultaneous feedback of forces and states allows the controller for an extra degree of freedom to approxima...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	202,918

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